Soft Set Based Quick Reduct Approach for Unsupervised Feature Selection

Feature Selection (FS) has been an active research area in Pattern Recognition, Statistics, and Data Mining Techniques. FS is a process to select most instructive features from the given data set. In this paper, we propose a new soft set based unsupervised feature selection algorithm. The reduction of attributes is achieved by using Soft Set Theory. Attributes are removed so that the reduced set provides the same predictive capability of the original set of features. The supremacy of the algorithm, in terms of speed and performance, is established extensively over various data sets. The result obtained using the proposed method is compared with existing rough set based unsupervised feature selection algorithm and this work demonstrates the efficiency of the proposed algorithm.